Databases are used to store information for an innumerable number of applications, including various commercial, industrial, technical, scientific and educational applications. As the reliance on information increases, both the volume of information stored in most databases, as well as the number of users wishing to access that information, likewise increases. Moreover, as the volume of information in a database, and the number of users wishing to access the database, increases, the amount of computing resources required to manage such a database increases as well.
Database management systems (DBMS's), which are the computer programs that are used to access the information stored in databases, therefore often require tremendous resources to handle the heavy workloads placed on such systems. As such, significant resources have been devoted to increasing the performance of database management systems with respect to processing searches, or queries, to databases.
Improvements to both computer hardware and software have improved the capacities of conventional database management systems. For example, in the hardware realm, increases in microprocessor performance, coupled with improved memory management systems, have improved the number of queries that a particular microprocessor can perform in a given unit of time. Furthermore, the use of multiple microprocessors and/or multiple networked computers has further increased the capacities of many database management systems.
From a software standpoint, the use of relational databases, which organize information into formally-defined tables consisting of rows and columns, and which are typically accessed using a standardized language such as Structured Query Language (SQL), has substantially improved processing efficiency, as well as substantially simplified the creation, organization, and extension of information within a database. Furthermore, significant development efforts have been directed toward query “optimization”, whereby the execution of particular searches, or queries, is optimized in an automated manner to minimize the amount of resources required to execute each query.
Through the incorporation of various hardware and software improvements, many high performance database management systems are able to handle hundreds or even thousands of queries each second, even on databases containing millions or billions of records. However, further increases in information volume and workload are inevitable, so continued advancements in database management systems are still required.
The variety and complexity of software applications that can be created using structured query language (SQL) rival that of many traditional programming languages and, accordingly, program development with SQL can be similar to traditional program development methods and techniques. In particular, a user can create or develop program structures called “user defined functions” (UDFs). Once such a structure is developed, it can be re-used in multiple SQL statements by simply referring to its name much like a function call in traditional programming languages.
The use of UDFs provide software developers the ability to generate functional code in ways that are familiar to them and to create code that is easier to read, understand, and maintain. An SQL query that includes references to UDFs will not be obscured by the multiple lines of actual code that form the UDF but, instead, will simply have a statement that references a single, appropriately-named UDF. Also, if programmatic changes are needed, multiple instances of code will not need to be modified. Rather, the one instance of a UDF can be revised and all references to that UDF will be able to take advantage of the revisions. One drawback, however, of using UDFs is that a UDF is less efficient during execution than native SQL statements. Accordingly, because of the overhead of retrieving and executing the code of a UDF, performance can suffer during runtime when the SQL query includes references to UDFs.